Vision-Language Meets the Skeleton: Progressively Distillation With Cross-Modal Knowledge for 3D Action Representation Learning

作者：Yang Chen, Tian He, Junfeng Fu, Ling Wang, Jingcai Guo, Ting Hu, Hong Cheng · 发表于：IEEE Transactions on Multimedia · 年份：2024 · DOI：10.1109/tmm.2024.3521718 · 被引用次数：6 · 研究领域：Multimodal Machine Learning Applications、Human Pose and Action Recognition、Hand Gesture Recognition Systems

Skeleton-based action representation learning aims to interpret and understand human behaviors by encoding the skeleton sequences, which can be categorized into two primary training paradigms: supervised learning and self-supervised learning. However, the former one-hot classification requires labor-intensive predefined action categories annotations, while the latter involves skeleton transformations (e.g., cropping) in the pretext tasks that may impair the skeleton structure. To address these challenges, we introduce a novel skeleton-based training framework (C$^{2}$VL) based onCross-modalContrastive learning that uses the progressive distillation to learn task-agnostic human skeleton action representation from theVision-Language knowledge prompts. Specifically, we establish the vision-language action concept space through vision-language knowledge prompts generated by pre-trained large multimodal models (LMMs), which enrich the fine-grained details that the skeleton action space lacks. Moreover, we propose the intra-modal self-similarity and inter-modal cross-consistency softened targets in the cross-modal representation learning process to progressively control and guide the degree of pulling vision-language knowledge prompts and corresponding skeletons closer. These soft instance discrimination and self-knowledge distillation strategies contribute to the learning of better skeleton-based action representations from the noisy skeleton-vision-language pairs. During the infe...